Accession Number : ADA306685
Title : Recognition of the Multi Specularity Objects using the Eigen-Window,
Corporate Author : CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE
Personal Author(s) : Ohba, Kohtaro ; Ikeuchi, Katsushi
PDF Url : ADA306685
Report Date : 29 FEB 1996
Pagination or Media Count : 30
Abstract : This paper describes a method for recognizing partially occluded objects for bin picking tasks using the eigen space analysis. Although effective in recognizing an isolated object, as was shown by Murase and Nayar; the current method cannot be applied to partially occluded objects that are typical in bin picking tasks. The analysis also requires that the object is centered in an image before recognition. These limitations of the eigen space analysis are due to the fact that the whole appearance of an object is utilized as a template for the analysis. We propose a new method, referred to as the 'eigen-window' method, that stores multiple partial appearances of an object in an eigen space. Such partial appearances require a large number of memory space. A similarity measure among windows is developed to eliminate redundant windows and thereby reduce memory requirement. Using a pose clustering method among windows, the method determines the pose of an object and the object type of itself. We have implemented the method and verify the validity of the method.
Descriptors : *COMPUTER VISION, REQUIREMENTS, VALIDATION, EIGENVALUES, MEMORY DEVICES, CLUSTERING, PATTERN RECOGNITION.
Subject Categories : Bionics
Distribution Statement : APPROVED FOR PUBLIC RELEASE